Rapidly increasing utilization of X-ray CT has heightened concerns about the collective radiation exposure to the population as a whole, and about potential risks to patients undergoing recurrent imaging for chronic conditions or persistent complaints. These concerns have generated a great deal of attention to practical radiation dose reduction strategies. For example, a recent addition to the commercially-available dose-reduction arsenal is on iterative reconstruction, which uses reduced tube current, and applies nonlinear procedures to denoise the resulting images. However, such approaches can be limited by the minimum useful tube current (e.g., the photon starvation problem), and in practice, only moderate dose reductions of up to about 30-40% below the prior state of the art. Recent advances in detector technology can offer similar reductions.
Radiation exposure has received much attention of late in the medical literature and lay media. It is commonly recognized that computed tomography (CT) has tremendously advanced our diagnostic capabilities broadly throughout medicine. These diagnostic benefits have combined with widespread availability and rapidity of scanning to produce marked increases in CT utilization, estimated at approximately 69 million CT scans per year in the US. However, rapidly increasing utilization has heightened concerns about the collective radiation exposure to the population as a whole, and about potential risks to patients undergoing recurrent imaging for chronic conditions or persistent complaints.
CT has received a lot of scrutiny because of its relatively high radiation dose per exam. While it comprises only about 17% of all medical imaging procedures, it produces approximately half of the population's medical radiation exposure, with nuclear medicine contributing approximately one quarter of the collective dose to the population, and fluoroscopy and conventional x-ray exams accounting for the remainder (See Refs. 5 and 6).
Various strategies have been proposed to reduce radiation exposure to the population as a whole, and to individual patients (See Refs. 7 and 8). Due to challenges of education and dissemination, the most successful and widely implemented dose-reduction measures can be those that perform with little need for end-user optimization. Examples of currently used measures are below.
Exemplary automated tube current modulation: Automated tube current modulation or dose modulation, can adjust the x-ray tube output mAs to the patient's anatomy in order to maintain a desired level of image quality (See Refs. 10 and 11). Greater mAs can be used in areas with greater x-ray attenuating path-lengths such as the shoulders or the pelvis, and lower mAs can be used in regions containing less attenuating material such as the lungs.
Adaptive Collimation: The development of wider detector arrays facilitating rapid anatomic coverage has had the unintended consequence of increasing z-overscanning, in which the anatomy above and below the desired scan range can be inadvertently irradiated. Implementation of adaptive collimators that open and close asymmetrically as the patient can be moved through the gantry can mostly eliminate this z-overscanning, with dose savings on the order of 10-35%, depending on the detector configuration and scan length (See Ref. 12).
Iterative Reconstruction: Iterative reconstruction procedures can solve an optimization problem (See Ref. 13) in which the cost function can be designed to reduce noise or artifacts resulting from reductions in x-ray tube current. Iterative reconstruction procedures can transform back and forth between the raw data “projection-space” and the image domain with each iteration, but, as this approach can be computationally expensive, most manufacturers have implemented more rapid shortcut procedures designed to achieve similar ends. Implementation details vary between manufacturers, but generally involve a variety of procedures to shift some of the iterative “correction steps” into the raw data or image domains, combined with advanced modeling of the CT acquisition system, and nonlinear image filtering, to reduce noise in homogeneous regions while attempting to preserve anatomic edge information. The noise reduction effected by iterative reconstruction techniques can enable substantial reductions in radiation exposure. The somewhat different noise texture of the resultant images can need some acclimatization on the part of radiologists, but in practical routine clinical use, dose reductions on the order of 20-40% can be typical (See Ref. 16).
Reduced kVp imaging: In many applications, reduced kVp can result in substantial dose savings. The greatest benefits can be realized in vascular imaging applications, as iodine attenuates lower energy x-rays more strongly than higher-energy x-rays. Adoption of low kVp approaches has been hastened through approaches such as CarekV which can direct the CT scanner to select the kVp setting that can result in the lowest dose to the patient, while meeting user-selected image quality constraints and inherent x-ray tube output limits.
Detector technology advances: CT manufacturers have spent substantial effort optimizing the materials (e.g. Siemens UFC detectors) and construction of their detector elements to maximize the conversion of incident x-rays to meaningful signal. Most recently, analog to digital conversion functionality has been moved to chips located on the detector elements themselves (e.g., Siemens Stellar detectors), thus reducing electronic noise. This has been shown to permit dose reductions on the order of about 30%.
Each of the dose reduction strategies above has the potential to reduce a dose incrementally from previous techniques. Some of these techniques may only be appropriate in select circumstances (e.g. dose reductions from tube current modulation for small patients and low kVp for vascular imaging particularly in smaller patients). In other cases, multiple techniques can be used in combination, producing multiplicative dose saving effects (See Ref. 18) (e.g. four 30% dose reduction strategies can in principle be used in combination to effect a 76% dose reduction relative to the starting point). However, existing methods cannot achieve routine submillisievert scanning in the majority of patients, and submillisievert scans remain elusive in almost all abdominal applications. Signature low dose results often involve small patients scanned at low kVp in the chest (e.g., where inherent x-ray attenuation can be low), but doses can be substantively higher in the abdomen and for typical patient sizes.
An alternative approach to reduce radiation dose in CT is the application of compressed sensing (“CS”) techniques (See Refs. 19 and 20). CS can exploit image compressibility to reconstruct an image with full information from a reduced set of incoherent measurements sampled below the Nyquist-Shannon rate. The reconstruction can also be iterative, but in contrast to the iterative reconstruction techniques mentioned above which denoise the fully-sampled images acquired with lower tube-current, the cost function can be designed to enforce sparsity in the space where the image can be compressible. Significant reductions in the number of measurements can be accomplished if the image can be sparse in a known transform domain (e.g., sparsity condition) and if the resulting aliasing artifacts can add incoherently to the sparse image representation (e.g., incoherence condition). CT presents favorable conditions for the application of CS since (a) medical images are naturally compressible by various transforms which lead to sparse representations, and (b) discarding projections can result in low-value streaking artifacts that add incoherently to the image representation.
Some current radiation dose reduction techniques in CT can rely on techniques to reduce the x-ray tube output throughout the CT scan acquisition (e.g., without rapid modulations). In some cases, nonlinear reconstruction techniques (e.g., iterative reconstruction or nonlinear filtering methods) can be used to reduce noise in the resulting images. However, there are currently no CT techniques that can undersample the x-ray projection data itself, which can be essential for making effective use of techniques such as compressed sensing to further reduce CT radiation dose beyond current limits. Thus, it may be beneficial to provide an exemplary system, method, and computer accessible medium that can modulate x-ray beam intensity for use with CS reconstruction, and which can overcome at least some of the deficiencies described herein above.